LoRA & QLoRA Explained In-Depth | Finetuning LLM Using PEFT Techniques

Unveiling the Power of LoRA & QLoRA - The Game-Changing Techniques for Fine-Tuning LLMs! 🧠💡 Discover how Low-Rank Adaptation and Quantized LoRA are revolutionizing the way we enhance machine intelligence.
Join us on this journey as we delve into the intricate world of low order rank adaptation and the groundbreaking concept of quantized LoRA. 💡 From the basics to the nitty-gritty details, we've got you covered!
Timestamps:
00:00 - Intro & Demo
00:22 - Pre Trained Model
01:05 - GPT VS ChatGPT
01:31 - GPT Family
01:58 - Safety Finetuning
02:42 - Task Specific Finetuning
03:37 - Neural Network with Weights
04:40 - Mistral Example
05:33 - Full Parameter Finetuning
06:22 - Full Parameter Finetuning Downsides
06:45- Mistral Model Memory Requirement
08:06- How is LoRA different
10:00- LoRA Paper
10:00- Matrix Decomposition
11:19- Number of trainable Parameters
13:33- What is QLoRA
14:50- QLoRA VS LoRA Implementation
19:51- QLoRA Important components
21:33- Choose Between QLoRA & LoRA
22:10 - Thanks
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[@harshays2873]

Good bro, very informative, Thanks...!!!

[@hrishibanik2522]

❤